1. Field of the Invention
The present invention relates to an on-line handwritten character recognition method for comparing an input pattern of a character which is handwritten in an arbitrary stroke-number and an arbitrary stroke-order with reference patterns which have correct stroke-numbers and correct stroke-orders and which are prepared for each character category to automatically determine a specific character category corresponding to the input pattern.
2. Prior Art
As a conventional on-line handwritten character recognition technique for improving robustness against handwriting distortion, there is a technique in which expected handwriting distortion, i.e., a variation in inclination or specific running handwriting, is registered in advance.
Thereafter, the following technique is provided. An input pattern and reference patterns are transformed into one-stroke patterns on the assumption that a stroke-order is correct, and pattern matching is performed such that overlapping between feature points is maximized in size while scale change in the time-axis direction is allowed using dynamic programming technique.
The following technique is also provided. That is, as reference patterns, not only average sizes of a large number of handwritten character patterns belonging to character categories, but also information related to handwriting distortion such as a covariance matrix of positional coordinates of feature points constituting each character are stored. Then, a statistical discriminant measure is used between the reference patterns and an input pattern.
However, the conventional handwritten character recognition technique in which expected handwriting distortion is registered has an essential limit because the conventional technique cannot cope with unexpected distortion.
In the conventional handwritten character recognition technique using the dynamic programming technique, large handwriting distortion cannot be entirely absorbed by the scale change in the time-axis direction, an enormous time is disadvantageously required for processing.
In the conventional handwriting character recognition technique using the statistic discriminant measure, an enormous number of handwritten character patterns must be collected to obtain stable statistic information in advance. In addition, since not only average values, but also information such as a covariance matrix are stored as reference patterns, the dictionary size for the reference patterns is considerably increased.
In order to solve the above problems, a technique described in Japanese Patent Application Laid-Open No. 63-206881. The outline of this technique is as follows. Only average position coordinates of each feature point of a plurality of handwritten character categories which are written in a correct stroke-number and a correct stroke-order are stored as a reference pattern of each character category, and an input pattern in which position coordinates of each feature point are stored with respect to a character handwritten in an arbitrary stroke-number and an arbitrary stroke-order is constituted. A one-to-one feature point correspondence between the input pattern and the reference patterns of character categories is determined. Such affine transformation operation is iteratively performed that overlapping between the feature points in a predetermined portion near each feature point of a reference pattern and the corresponding feature points of the input pattern is maximized. Thus, after distorting the reference patterns, stable pattern matching is performed.
This technique can theoretically solve the above problems. That is, the technique can absorb arbitrary handwriting distortion by iteratively performing the local affine transformation operation, and has a small dictionary size for reference patterns. However, the technique still has the following problems. That is, trial and error must be performed to determine the optimum size of the predetermined portion near which the local affine operation is performed and to control the number of iterative operations for preventing excessive absorption of handwriting distortion, and enormous time is required to iteratively perform an optimum local affine transformation operation for each feature point.
As described above, in the conventional on-line handwritten character recognition technique for improving robustness against handwriting distortion, an essential means which can absorb large or unexpected handwriting distortion and can minimize a dictionary size and a processing time has not been obtained.